Research map / Multi-agent systems and games / Human-AI and goal-conditioned
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use TrafficPredict instead of Social Attention | Trajectory prediction for heterogeneous traffic agents · TrafficPredict dataset, all agents | Average displacement error: 0.110 -> 0.085 | Helped |
| use TrafficPredict instead of Social Attention | Trajectory prediction for heterogeneous traffic agents · TrafficPredict dataset, all agents | Final displacement error: 0.178 -> 0.141 | Helped |
| add the category layer (ablation) | TrafficPredict without the category layer · TrafficPredict dataset, all agents | Average displacement error: 0.113 -> 0.108 | Helped |
| add the category layer (ablation) | TrafficPredict without the category layer · TrafficPredict dataset, all agents | Final displacement error: 0.187 -> 0.165 | Helped |
| add self-attention to the category layer (ablation) | TrafficPredict with the category layer · TrafficPredict dataset, all agents | Average displacement error: 0.108 -> 0.085 | Helped |
| add self-attention to the category layer (ablation) | TrafficPredict with the category layer · TrafficPredict dataset, all agents | Final displacement error: 0.165 -> 0.141 | Helped |
Closest papers by meaning
- TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions (CVPR 2019 · similarity 0.88)
- Trajeglish: Traffic Modeling as Next-Token Prediction (ICLR 2024 · similarity 0.87)
- LaKD: Length-agnostic Knowledge Distillation for Trajectory Prediction with Any Length Observations (NeurIPS 2024 · similarity 0.87)
- SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction (ICLR 2025 · similarity 0.86)
- NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving (AAAI 2025 · similarity 0.85)
- DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States (NeurIPS 2024 · similarity 0.85)
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